This is Philosophy of Science. Franz-Peter Griesmaier
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Название: This is Philosophy of Science
Автор: Franz-Peter Griesmaier
Издательство: John Wiley & Sons Limited
Жанр: Математика
isbn: 9781119758006
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Suppose you are sitting in your living room and suddenly hear strange noises coming from your attic – a quick succession of what sounds like little taps and then a rumbling noise. You consider two hypotheses: First, the noise is produced by gremlins from outer space that have landed on your roof and are now bowling in your attic. Call this hypothesis G. Second, the noise is produced by the neighbor’s cat, which got into your attic and is trying to catch mice, but keeps running into the books you have stacked up there. Call this hypothesis C. Clearly, C is better than G, although G is an explanation of sorts: If it were true, then the probability of hearing those noises would be quite high. However, the same is true of C, and since C is more plausible than G in light of all the other things you believe, C is clearly the better explanation. You then infer that the best explanation is the most probable one and thus accept C. You have inferred the best of the explanations under consideration; this is IBE.
Several things are worth commenting on. First, both G and C make the observation probable – both bowling gremlins and mouse-chasing cats could produce those noises you hear. Second, both C and G might be false. Maybe it’s neither cats nor gremlins, but it’s some neighborhood kids playing a practical joke. Third, and related to the last point, what hypothesis counts as the best is partially determined by which ones you can come up with. In other words, the best hypothesis from among those we thought of need not be a very good one, all things considered. Often, we miss an even better hypothesis, as we know from the history of science and discuss further in Chapter 12. In fact, the history of scientific progress is one of not only gathering more evidence but improving explanations such that was once the “best” is supplanted by something better.
Thus, even if you are very creative in generating hypotheses, you might generate really awful ones and shouldn’t believe any one of those. This has prompted some to eschew the use of IBE altogether, especially insofar as it pertains to unobservables (things we can’t directly see or otherwise sense, such as electrons or magnetic forces). In short, IBE can provide some reason for accepting a claim (we use it in forensic sciences all the time, for example, when we try to find the person whose presence is the best explanation of all the clues, and infer that the person who best fits the clues is the perpetrator), but it certainly doesn’t guarantee our knowing the truth.
Finally, it is important to point out that IBE cannot be reduced to other forms of inductive inference. Inferring the presence of a stray cat in my attic as the best explanation of the noise I am hearing does not (need to) involve prior observations of stray cats in my attic and their behavior. Thus, this inference is different from a statistical inference, which, if you recall the example of koalas, does rely on observations of the feeding habits of a number of koalas to infer something about what other koalas will eat. Neither am I trying to establish any sort of regularity when I infer that a cat must have gotten into my attic. I am simply interested in explaining this particular and odd event by evaluating various hypotheses as to their plausibility in light of my background knowledge about cats, none of which I need to have oberved in an attic.
Let’s recap what we discussed in this section. We distinguished between conclusive and defeasible reasons. Along with that distinction comes another one, namely, the distinction between deductive and inductive reasoning. The former can be characterized as an inference from the implicit to the explicit, while the latter is an inference from the observed to the unobserved. The following diagram (Figure 1.1) shows the distinctions you should keep in mind:3
Figure 1.1 Forms of reasoning.
1.3 Knowledge and Truth
Scientific inquiry is often characterized as an especially promising way to increase our knowledge of the world by discovering more and more truths about it. But what exactly is knowledge, and what is truth? To answer the first question, we can start by determining what the difference between mere belief and knowledge is. Suppose Marvin strongly believes that atoms are held together with hooks. Does Marvin know this to be the case? It seems not, for the simple reason that it is false. We can’t know what’s false. Of course, we can believe what’s false, and we can falsely believe that we know something even though it is in fact false. But we can’t know that 2 + 3 = 78. Thus, we can conclude that in contrast to mere belief, a belief that counts as knowledge has to be true.
But being true is not enough for a belief to count as knowledge. Suppose I am terrified of snakes, flipping a coin every morning before entering the lab to see whether I have to use protective gear. This morning, the coin landed heads up, which means, to me, that there is a snake in the lab. Moreover, there in fact is a snake – unnoticed by me, a practical joker snuck it into the building. Do I know that there is a snake? It seems not. That the coin landed heads up doesn’t provide a good reason for believing that there is a snake. And yet it is true. This shows that for knowledge, we don’t just need truth – we need good reasons as well.
Putting it all together, knowledge amounts to true beliefs that are based on good epistemic reasons. Some of the readers might be aware of the fact that over the last several decades, it has become clear that even true beliefs for which I have good reasons might not automatically qualify as knowledge – something else is needed, at least in the kind of instances usually called Gettier cases.4 However, because we encounter such cases only rarely, if at all, during scientific research, we won’t discuss them here. In the next couple of chapters, we will instead explore the notion of evidence and how it is supposed to yield good reasons for our beliefs about the world, thus providing us with one ingredient of scientific knowledge.
The second ingredient is truth. There are many competing theories of what truth is, and there is an important debate in the philosophy of science whether or not our best theories are true. We can’t explore the first question here, but we will explore the debate about the alleged truth of scientific theories in Chapter 12. For now, we can just stipulate that by a true theory, or a true belief for that matter, we mean a theory (or a belief) that corresponds to the facts in the world. For example, the hypothesis that you are reading this sentence right now is true because it corresponds to the fact that you are reading it.
1.4 Facts, Hypotheses, and Theories
This may be a good point for some remarks about the relation between facts, hypotheses, and theories. To begin with, facts are actual states of affairs in the world, or how things are. For example, there is the fact that you are reading this sentence right now. This is something that is happening in the world – you reading this sentence is among all the facts that together determine the world. In contrast, hypotheses and theories are (often) linguistic expressions of our beliefs: They consist of sentences purporting to report the facts. For example, the sentence “You are reading this chapter right now” is a hypothesis about you and what you are doing. The hypothesis is right now true (you are still reading, aren’t you?), but once you get bored and look for your friends on social media, it will become false. And this happens because the facts will have changed.
The difference between fact and either theory or hypothesis can be easily overlooked. For example, in discussions about evolutionary biology and creationism, one often hears creationists complain that evolution is just a hypothesis, while the evolutionists call the theory of evolution a proven СКАЧАТЬ